Scenario Analysis Method Research Articles

Management of public financial debt in conditions of unbalanced supply and demand of energy resources

Financial debt management in the context of the energy crisis is an urgent problem that requires a strategic approach in countries with an unstable energy system. The imbalance between the supply and demand of energy resources generation creates a trap in which the growth of energy shortages leads to increased import costs, higher prices for end consumers and increased financial burden on the state. This, in turn, can provoke inflationary pressure, a decrease in the competitiveness of the national economy, an increase in public debt and social tension due to an increase in the cost of living and a decrease in the level of energy security. The research used the methods of scenario analysis, descriptive statistics, econometric, monographic and comparative analysis. The purpose of the article is to analyze the impact of energy crises on the state of state financial support, to determine ways to minimize risks and ensure financial stability. In the course of the study, the degree of influence of the imbalance of demand and supply of energy resources on the state financial debt was determined through the use of such tools as: analysis of market conditions, assessment of the level of demand and supply for energy resources. The paradigm of an integrated approach to the management of public debt in the context of energy crises is characterized, which is based on an integrated, multidimensional analysis and management of interrelationships between various economic, financial, social and political factors. It is emphasized that consideration of the energy sector should not take place in isolation, but in the context of its interaction with the financial capabilities of other sectors of the economy. Practical recommendations include risk assessment models related to energy crises and methods of stabilizing public finances, which can be used in the operational management of the state's financial debt. The method of integration and coordination of energy, economic, fiscal and social indicators is proposed for determining effective financial instruments and mechanisms for optimizing budget expenditures. It is concluded that in the conditions of imbalance of energy demand and supply, management of financial debt requires a strategic approach based on investments in clean energy, improvement of energy efficiency and accurate forecasting

Parameter adaptive stochastic model predictive control for wind–solar–hydrogen coupled power system

With the increasing global energy scarcity and environmental concerns, the wind–solar–hydrogen (WSH) coupled system has garnered widespread attention as an efficient and eco-friendly renewable energy solution. Addressing the impact of uncertainty on the source and load of the coupled system concerning its power balance, a parameter adaptive stochastic model predictive control (PAS-MPC) power regulation strategy based on the scenario analysis method is proposed herein. In order to characterize the probabilistic information about the uncertainty on both sides of the system, scenario generation and reduction techniques are used to obtain classical scenarios of wind and solar outputs as well as electrical loads as inputs to PAS-MPC. With the aim of optimizing the computational time constant of SMPC, the parameter adaptive method is proposed to change the prediction time domain and control time domain of the system. Simulation results demonstrate the effectiveness of PAS-MPC in addressing uncertainties on the source and load sides of the WSH coupled system. Optimization results reveal that PAS-MPC considerably improves the system power balance compared to SMPC. Compared to the conventional model predictive control (MPC), PAS-MPC effectively mitigates high-power state of the controllable equipment of the system, thereby enhancing its lifecycle.

Decision-making method for low-carbon transformation technology in existing residential areas based on knowledge graph and scenario analysis methods

ABSTRACT Cities are the main contributors to carbon dioxide emissions, and communities are crucial for implementing urban low-carbon emissions. As a result of the controversies surrounding existing transformation technologies and the varying conditions faced by different targets for transformation, an effective method is required for application in specific target communities to achieve good carbon reduction effects. We propose a method that utilizes the knowledge graph and scenario analysis to systematically screen and evaluate community low-carbon technologies. First, a comprehensive knowledge map spanning multiple fields was constructed, and the correlations were analyzed using CiteSpace. Additionally, we categorized the transformation technologies into nine scenarios to assess their effectiveness and feasibility across different community contexts. Finally, the near-zero carbon emission community pilot area of Shenzhen City was selected as a case study, and the method was applied as a demonstration. The total carbon emissions of the pilot community were reduced by 43% after applying the proposed method. This method is helpful for technical screening at the low-carbon transformation stage and also provides a useful reference for the low-carbon construction stage of communities.

Simulation of hydrogen transportation development path and carbon emission reduction path based on LEAP model - A case study of Beijing-Tianjin-Hebei Region

Hydrogen-fuelled vehicles have attracted significant attention around the world recent years as the main form of hydrogen transportation. Chinese government in September 2020 has introduced corresponding supporting policies. In particular, Beijing-Tianjin-Hebei urban agglomeration is the first batch of pilot cities. To research the development of hydrogen transportation and carbon emission reduction paths in Beijing-Tianjin-Hebei urban agglomeration, we construct LEAP-Beijing-Tianjin-Hebei Urban Agglomeration Hydrogen Transportation model, and simulate it based on the scenario analysis method. The results show that the total hydrogen demand reaches 7,975,860 tonnes in the year 2060. And the freight transportation sector is the largest hydrogen consumer in transportation. For carbon emission reduction, transportation sector under different scenarios could achieve net-zero emission in year 2060, with total emission reduction 431.19 million metric tonnes. We suggest that the promotion of hydrogen transportation should be carried out in stages. Early large-scale development of hydrogen-fuelled vehicles in sectors such as passenger cars and specialized vehicles is not significant in emission reduction, and it is more appropriate to start with medium and large-sized commercial vehicles. Gradually extend to the whole transportation sector after the technology of hydrogen-fuelled vehicles is mature and the supply capacity of clean hydrogen is improved.

Coordinated optimization of configuration and operation of a photovoltaic integrated building cooling system with electricity and ice storages under source-load uncertainties

Solar photovoltaic integration into buildings provides an effective and attractive method to decrease fossil energy consumption and reduce greenhouse gases emission. This study proposes a flexible photovoltaic integrated building cooling system with electricity and ice storages to address the challenges posed by the instability of solar energy and the integration and coordination of multiple components. Uncertainties in solar irradiance, ambient temperature, and cooling load are characterized using a scenario analysis method that combines Latin Hypercube Sampling and the K-Means++ algorithm. Then, aiming to achieve the minimal total cost, including investment, operational, and CO2 emission costs, a two-layer stochastic optimization model is developed to simultaneously determine the optimal capacities and operational strategies for each device. Genetic algorithm and mixed integer linear programming are, respectively, used to solve the two-layer optimization problem. The comparisons between optimum schemes with and without CO2 emission cost demonstrates that when the base chiller’s capacities are 1179 kW and 1081 kW in cases 1 and 2 respectively, it can independently meet 72.37 % and 61.27 % of the cooling load demand throughout the cooling season. In addition, the optimal capacity of photovoltaics, considering CO2 emission, increases by 109.7 %, and its penetration rate reaches 18.3 %, which is 9.7 % higher than the optimal scheme not considering CO2 emission. However, the annualized investment cost increases by 52.3 %. The developed methods provide effective tools for the system design and operation management of solar energy integrated building cooling system.

A Study of Anticipatory Failure Determination (AFD) based on Scenario Analysis Methods

Theory of Inventive Problem Solving (TRIZ) is a methodological algorithm that helps solve complex problems by generating original ideas. The Anticipatory Failure Determination (AFD) method is one of the problem-solving methodologies within TRIZ. It is a creative way to solve problems when you cannot find the cause of a problem or observe the situation in which it occurs. This study aimed to investigate the effectiveness of the Anticipatory Failure Determination (AFD) method, which is one of the problem solving methodologies within the Theory of Inventive Problem Solving (TRIZ). TRIZ is a methodological algorithm designed to address complex problems by stimulating the generation of original ideas. It involves a shift in thinking: instead of looking for the cause of a problem (i.e., "why does it happen?"), it looks for ideas to solve the problem (i.e., "how can I make it happen?") by formulating a hypothesis and testing the hypothesis. In doing so, they explore and leverage resources that exist within or adjacent to the system to support the failure hypothesis. A resource is anything that is used to fulfill the ideal final result. In this study, scenario analysis, a methodology synonymous with foresight, is integrated into the AFD framework to enhance problem-solving capabilities. By incorporating scenario analysis, the formulation of failure hypotheses crucial to the AFD process is facilitated. Additionally, a case study demonstrating the application of AFD augmented by scenario analysis is presented, showcasing its efficacy in resolving complex problems. This integration aims to proactively mitigate future risks and prevent potential damages. Through this research, a comprehensive understanding of how scenario analysis enriches the AFD methodology is provided, empowering practitioners to pre-emptively address emerging challenges. Ultimately, the goal is to fortify problem-solving capacities and foster resilience in the face of evolving complexities.

The phenomenon of the outsider as the main character in modern popular culture (on the example of series and films of the 1990s and later)

Introduction: studying this phenomenon allows to understand the reasons for the popularity of this character in popular culture. The purpose of the article is to conduct an analysis of the phenomenon in the context of modern mass culture by analysing modern films and series, to reveal its characteristics and significance for the viewer, as well as to consider the impact of this phenomenon on modern society. Method: the following scientific methods were used in this study: the method of content analysis, the method of scenario analysis, and the method of interpretive analysis. Results: the results of the study showed how they are depicted in mass culture, how their role has changed over time, their influence on the perception of social and cultural problems, as well as their influence on the formation of an individual’s identity and self-determination. The study examines aspects such as conformity, marginalization, and exclusion of outsiders in popular culture. The research uses methods of studying the content of TV series and films, their storylines, dialogues, and images dedicated to the research topic. The analysis of intertextual elements in the scripts of films and series was carried out, which allowed revealing the connections between different works and cultural contexts. An analysis of the social aspects of the phenomenon, such as marginalization and exclusion, was also carried out. Conclusion: the research has practical significance for cinema and television, as it can help content creators in choosing the main character and shaping his character

Energy supply structure optimization of integrated energy system considering load uncertainty at the planning stage

Under the trend of global carbon neutrality, the integrated energy system with the characteristics of multi-energy scheduling and gradient utilizing will be widely constructed and applied in the future energy market. For the construction of energy systems in emerging building complex, this paper analyzes the load probability characteristics of regional building complex at the planning stage, and conducts an aggregation analysis of its multiple uncertainties, and obtains the conclusion that the load factor at time t obeys the normal distribution. In order to formulate this uncertainty, this paper combines the case study with the scenario analysis method containing scenario generation and reduction to transform the stochastic programming model into several deterministic models and analyses the discrepancies of the optimization results under different objectives and different load variances. The results show that after considering load-side uncertainty, the total system capacity increases by 7 %, the total investment under the minimum investment objective by 4 %, and the carbon emission under the minimum carbon emission objective by 3 %. In addition, the system needs to pay 32.8 % of the total investment increment to obtain 80 % carbon reduction when adopting carbon emissions as the objective.

Carbon emissions path of public buildings based on LEAP model in Changsha city (China)

China is currently the world's top carbon emitter due to the acceleration of urbanization, the rapid growth of energy consumption and the rise in carbon emissions, with the construction industry leading the three energy sectors in terms of emission reduction potential. Among the various sectors, the construction industry holds immense potential for emission reduction, with public buildings playing a crucial role. In China, the expansion of public building spaces is closely linked to the pace of urbanization and contributes to a rising share of total building area. Public buildings, characterized by high energy usage, offer a great opportunity for energy conservation and emission reduction. Changsha is experiencing rapid growth and change, and the city is becoming more industrialized and urbanized, which leads to a prominent contradiction between energy supply and demand. There is also great potential for energy conservation and emission reduction in public buildings. Additionally, Changsha is an important node city in the Yangtze River urban agglomeration and the middle reaches of the Yangtze River Economic Belt. It is a typical area that has hot summer and cold winter, thus Changsha is selected as the research case to examine the carbon emission path of public buildings. The primary influencing factors of carbon emissions of public buildings in Changsha City are determined by using the LMDI factor decomposition method addition model. The scenario analysis method is adopted to set the two energy consumption forecast scenarios, which are the baseline scenario and the green scenario. Based on LEAP model and GREAT framework structure, the LEAP prediction model is constructed to predict the carbon emissions of public buildings in Changsha City from 2021 to 2035. The Baseline scenario indicates that while carbon emissions from public buildings in Changsha continue to rise, they are expected to decline starting in 2030. The peak carbon emission of 34.279 million tons of CO2 is predicted for 2032. In contrast, the Green scenario reveals a significantly slower growth rate in carbon emissions from public buildings, with the peak carbon emission projected to reach 27.054 million tons in 2030. This aligns with China's national goal. The findings of this study support the accuracy of the long-term prediction of the model, and offer recommendations for energy conservation and emission reduction in Changsha. This study also provides an example and certain guidance for other provinces and cities that have similar development models and climate conditions. Pertinent development strategies serve as a point of reference for other typical hot summer and cold winter regions.

CONTROL ALGORITHM FOR IMPLEMENTING THE MATHEMATICAL MODEL OF SCENARIO MANAGEMENT AS A TOOL FOR ENSURING SECURITY OF A STRATEGIC FACILITY

The article first considers the mathematical model of scenario management as a security tool for a strategic object. Then, it develops the structure of the control algorithm for implementing this mathematical model. The study concludes with the structure of the algorithm and the need to establish basic procedures for its application. Ukraine at war faces a wide range of tasks to protect the state, including ensuring the safety of critical infrastructure objects (CIO). Many methods, namely scenario analysis methods, can be applied to ensure the security of the CIO and other strategic objects. Based on these methods, the authors develop a mathematical model of scenario management as a security tool for a strategic object. The problem lies in the practical use of the developed mathematical model to ensure the safety of a specific CIO. Based on the above, the article aims to create a control algorithm for the implementation of a mathematical model of scenario management as a tool for ensuring the security of a strategic object. For this purpose, the study sets such objectives as considering the mathematical model of scenario management as a tool for ensuring the security of a strategic object, developing the structure of the control algorithm for implementing this mathematical model, analysing it, and drawing a conclusion about the structure of the algorithm and the need to establish basic procedures for its application. Thus, the control algorithm for implementing the mathematical model of scenario management as a tool for ensuring the security of a strategic object is a hierarchical structure of thirteen blocks (or modules) located on twelve hierarchical levels, connected by direct and feedback links. It ensures the formation of private tasks of securing a strategic object, choosing a scenario management option, countering terrorist influence, eliminating consequences, preparing for a repeated attack, assessing the object’s state, and clarifying private tasks of ensuring security. For the correct practical use of this control algorithm when solving tasks related to the protection of strategic objects and critical infrastructure facilities, it is necessary to develop detailed procedures for its application. Keywords: critical infrastructure object, scenario management, algorithm, mathematical model, emergency.

Exploring the green and low-carbon development pathway for an energy-intensive industrial park in China

Energy-intensive industrial parks (IPs) with the distinct characteristics of high energy consumption, substantial greenhouse gas (GHG) emissions, and huge pollution, are facing enormous pressure to realize the synergistic mitigation of GHG and air pollutants. Exploring the green and low-carbon transformation of energy-intensive IPs is urgently required to achieve the dual strategies of “carbon peaking and carbon neutrality” and “Beautiful China”. Taking a typical energy-intensive industrial park (IP) in Henan Province—the Red Flag Cannel Industrial Park (RFCP) as the research object, an integrated evaluation framework including multi-objective optimization model alongside the synergistic mitigation potential of carbon and air pollutant emissions, was established in the energy-intensive IPs. Five scenarios were designed to quantify the GHG and air pollutant mitigation potential, while synergistic emission reduction control effects were evaluated under four measures of industrial structure adjustment, energy structure adjustment, energy efficiency improvement, and industrial symbiosis. The scenario analysis indicates that the integrated scenario (S4) shows the most significant emission reduction potential by 2030. Compared to the baseline scenario (S0), the GHG emission reduction is 11.66 Mt CO2eq (53% reduction rate), and the emission reduction rate of air pollutants ranges from 38% to 69% in S4. The findings indicate that industrial structure adjustment and energy structure adjustment are the primary measures for reducing GHG and air pollutants of IPs. Additionally, the evaluation of synergistic effect demonstrates that industrial structure adjustment shows the most significant synergistic effect on GHG and air pollutant by 2030. Finally, some policy recommendations regarding the transformation direction of energy-intensive IPs were proposed. This study will provide valuable references for the green and low-carbon development of energy-intensive IPs.

Collaborative modeling and optimization of energy hubs and multi-energy network considering hydrogen energy

To achieve the goal of carbon neutrality, integrated energy hubs (EHs) based on coupled energy sources such as electricity, gas and heat are gaining increasing attention due to their efficient and flexible energy supply characteristics. The introduction of integrated EHs may lead to the reconstruction of the existing superior energy network to accommodate the suitable flow of the new network. In this study, a multi-objective optimization model is proposed that synergistically considers the optimal configuration of EHs and the reconstruction of higher-level energy networks, with the objectives of minimizing total annual energy supply costs and CO2 emissions. First, an EH and multi-energy network (MEN) topology model considering hydrogen energy is developed. Secondly, the joint output of wind and solar power under typical scenarios is sampled and obtained by employing the Monte Carlo sampling method and the synchronous back-propagation scenario analysis method. To address the nonlinear constraint problem associated with multi-energy storage output, the Big-M method is introduced to improve the optimization capability of spatial-temporal coupling of energy storage. Moreover, the utilization potential of hydrogen and renewable energy is explored by considering constraints on the nonlinear dynamic pricing of hydrogen. Finally, a numerical analysis is conducted using an illustrative example. The Pareto frontier optimal results, derived from fuzzy theory membership functions, are achieved using both classical and heuristic algorithms. The classical mixed-integer linear programming algorithm yields relatively superior optimization outcomes in terms of annual energy costs and carbon emissions compared to the heuristic algorithm. However, it suffers from longer computation times.

Research on Short Term Prediction of Electricity Demand in Hubei Province

The balance of electricity supply and demand is the basic condition for promoting the economic development of our country and improving the living standards of the people, and scientific forecasting of electricity demand is the premise of ensuring the balance of electricity supply and demand. Hubei Province is superior in geographical location, and its electricity demand and economic aggregate both rank high in the country, but due to the direct impact of the epidemic, its electricity demand has been affected. This paper uses OLS regression to obtain the linear regression equation of the three indicators of GDP and the proportion of the secondary industry structure before the epidemic from 2005 to 2018, and uses the Gray Forecast Model-assisted scenario analysis method to analyze the electricity consumption of Hubei Province under the influence of multiple factors, and predicts the electricity consumption of Hubei Province from 2019 to 2021 after removing the impact of the epidemic, and makes a short-term forecast of the electricity demand of the province from 2024 to 2029.

History and future of water footprint in the Yangtze River Delta of China.

Quantifying the drivers of water footprint evolution in the Yangtze River Delta is vital for the optimization of China's total water consumption. The article aims to decompose and predict the water footprint of the Yangtze River Delta and provide policy recommendations for optimizing water use in the Yangtze River Delta. The paper applies the LMDI method to decompose the water footprint of the Yangtze River Delta and its provinces into five major drivers: water footprint structure, water use intensity, R&D scale, R&D efficiency, and population size. Furthermore, this paper combines scenario analysis and Monte Carlo simulation methods to predict the potential evolution trends of water footprint under the basic, general, and enhanced water conservation scenario, respectively. The results show that (1) the expansion of R&D scale is the main factor promoting the growth of water footprint, the improvement of R&D efficiency, and the reduction of water intensity are the main factors inhibiting the increase of water footprint, and the water footprint structure and population size have less influence on water footprint. (2) The evolution trend of water footprint of each province under three scenarios is different. Compared to the basic scenario, the water footprint decreases more in Shanghai, Zhejiang, and Anhui under the general and enhanced water conservation scenario. The increase in water footprint in Jiangsu under the enhanced scenario is smaller than that of the general water conservation scenario.

Exploring the impacts of woodland management on ecosystem services – a deliberative method

ABSTRACT There is a need for operational decision-making methodologies applicable at local management scales which are inclusive and enable the integration of plural values, knowledges and perspectives of co-produced ecosystem services. Here we describe a deliberative scenario analysis method using ‘management intervention bundles’ as distinct scenarios to assess the perceived impact of changing management on woodland ecosystem services. We used three hypothetical future management scenarios, Biodiversity Conservation, People Engagement and Austerity, alongside scenarios of the Past, Present and an existing Management Plan. We assessed the perceived impacts of these scenarios on 11 ecosystem services using local expert workshops in six sites across Scotland. The experts were chosen to represent a range of different perspectives, from biodiversity to the local economy, community concerns and recreation. Overall, Management Plan, Biodiversity Conservation and People Engagement scenarios performed significantly better than Past, Present and Austerity scenarios. Further quantitative and in-depth qualitative analysis revealed trade-offs and noteworthy patterns. We explore some of these key trade-offs and patterns and argue that our methodology has potential to be an effective tool for local managers to support local decision-making at management scales for co-produced ecosystem services. Our methodology enabled a diverse group of local experts to express and deliberate a range of values, experiences and viewpoints. This knowledge sharing and collective learning allowed the development of shared values and perspectives, which are thought to be critical in more equitable and inclusive decision-making.

Research on driving mechanism and prediction of electric power carbon emission in Gansu Province under dual-carbon target

The electric power industry is a key industry for the country to achieve the double carbon target. Its low carbon development has a double effect on this industry and helps other industries to achieve the carbon peak target. This paper firstly uses the IPCC inventory method to calculate carbon emissions in the production phase of the power industry in Gansu Province from 2000 to 2019, followed by the ridge regression method and the STIRPAT model to analyse the quantitative impact of six major drivers on carbon emissions, and finally, the scenario analysis method is used to forecast carbon emissions in this phase. The results show that the carbon emissions of Gansu Province show a trend of rising and then falling, and reached a peak of 65.66 million tons in 2013. For every 1% increase in population effect, urbanisation level, affluence, clean energy generation share, technology level and industrial structure, carbon emissions will grow by 4.939%, 0.625%, 0.224%, − 0.259%, 0.063% and 0.022% respectively. Because of the clean energy advantage in Gansu Province, the low-carbon development scenario will continue to have low carbon emissions during the scenario cycle, which can be reduced to 53.454 million tons in 2030; the baseline scenario will achieve a carbon peak in 2025, with a peak of 62.627 million tons; the economic development scenario has not achieved carbon peak during the scenario cycle, and carbon emissions will increase to 73.223 million tons in 2030.

Exploring the food-energy-water nexus in coupled natural-human systems under climate change with a fully integrated agent-based modeling framework

Managing water resources to meet increasing energy and food demands while maintaining environmental sustainability under climate change is a major challenge, especially when this nexus occurred in a coupled natural–human system (CNHS), where heterogeneous human activities affect the natural hydrologic cycle and vice versa. The relevant research has been limited by the lack of models that can effectively integrate human dynamics and hydrologic conditions with spatial details to examine co-evolutionary systems. To address this challenge, this paper develops a modeling framework that integrates an agent-based model (ABM; human behavior model) into a large-scale, process-based distributed hydrologic model to simulate human decisions endogenously in the hydrologic cycle. We then apply the Decision Scaling approach, an ex-post scenario analysis method, with our integrated model to study the bidirectional feedback of the CNHS under future changing climate conditions. With the Columbia River Basin (CRB) selected as the case study area, the calibration results show that the integrated model can simultaneously capture the historical irrigated water consumption and streamflow dynamics. Modeling results show that the trade-off between irrigated water consumption, hydropower generation, and streamflow will become more pronounced under hotter and wetter climate conditions at both the entire basin and regional (states and provinces) levels. Special attention should be given to “temperature thresholds” of different regions when the trade-off pattern started. The trade-off results can potentially inform the Columbia River Treaty renegotiation and provide insights for long-term water management policies.

An analysis of the impacts of land use change on the components of the water balance in the Central Rift Valley sub-basins in Ethiopia

Water resources are influenced by changes in land use and land cover (LULC), such as industrialization, urbanization, forestry, and agriculture. This study has aimed to analyze past and predicted LULC dynamics and their impacts on the components of the water balance in the Central Rift Valley (CRV) sub-basins in Ethiopia. The Soil and Water Assessment Tool (SWAT) and the Land Change Modeler (LCM) were employed to evaluate the impacts of past and future LULC dynamics in the Ketar, Meki and Shalla sub-basins. The SWAT models were calibrated with flow data from 1990 to 2001 and were validated with flows from 2004 to 2010, using SWAT-CUP in the SUFI-2 algorithm. LCM with Multi-Layer Perceptron (MLP) neural network method for land transition scenario analysis and a Markov Chain method for predictions, as well as SWAT models with fixing-changing methods for simulations, were used to evaluate the condition of hydrological processes under the influence of changes in LULC. The analyses resulted in an annual runoff variation from − 20.2 to 32.3%, water yield from − 10.9 to 13.3%, and evapotranspiration from − 4.4 to 14.4% in the sub-basins, due to changes in LULC. Integrated land use planning is recommended for the management of water resources.

Agent-Based Modelling for Scenario Analysis in Management Consulting

This paper investigates the modernization of a corporate practice called scenario analysis. It is widely used across the globe, originating from World War II, helping companies to gain a better understanding of potential future outcomes. In order to modernize scenario analysis, this paper will explore the novel approaches; of a hybrid development between agent-based modelling (ABM) and scenario analysis, the utilization of bottom-up and industry view approaches and introducing elements of human social behaviour. All these features are integrated in a single scenario analysis ABM that will model the fundamental philosophy behind the daily operations of a large corporation, such as Deloitte. This paper addresses more natural evolutions for scenarios, by looking at the firms' fundamental building blocks, rather than just an objective view based on the firms' financial statements. This model will focus upon accurate representations of the daily interactions of an enterprise, especially improving the explainability and transparency of causality in scenario analysis generated by the models, in order to achieve enduring agreements among executives about the firm's future. With this novel approach to scenario analysis, the modelling of a firm with the use of an ABM, there is the potential to provide more in-depth micro-observations of the future, rather than the conventional scenario analysis methods performed today.

Unveiling the Spatial-Temporal Characteristics and Driving Factors of Greenhouse Gases and Atmospheric Pollutants Emissions of Energy Consumption in Shandong Province, China

As the largest energy-consuming province in China, Shandong faces the dual task of greenhouse gas (GHG) reduction and atmospheric pollution control. Based on the latest activity data and updated emission factors, this study establishes a high-resolution emission inventory (5 km × 5 km) for GHGs and main atmospheric pollutants from the energy consumption sectors of Shandong Province from 2010 to 2021, quantifies the relationship between social economic factors and GHGs and atmospheric pollutants emissions using the expanded stochastic environmental impact assessment (STIRPAT) model, and forecasts the future emission trend with the help of the scenario analysis method. Results indicate that the electricity and transportation sectors are the main contributors to all pollutants. Spatially, the high value of pollutants is mainly concentrated in the urban agglomerations of central and eastern Shandong. Up to 72% of GHGs and 50% of air pollution emissions are attributed to the top 10% of emission grids. Emission peaks occur mainly in summer and winter due to straw burning, increased utilization of temperature-controlled facilities, and expansion of plant capacity. Population, energy consumption, the proportion of secondary industry, and energy consumption intensity are the most significant influencing factors for pollutant emissions. Scenario analysis results indicate Shandong province can reach its carbon peak in 2027 without sacrificing population growth or economic progress.